This invention relates to an arrangement for determining the identity of a provided character. The problem of designing a machine that can reliably recognize a wide range of character types has long fascinated engineers and computer scientists.
Indeed, machine identification of characters has become an important area of concern with the advent of electronic text processing and video graphics. Such a machine would have a variety of uses in the "office of the future". It could allow computers to "read" a wide variety of documents, from business letters and purchase orders to memos and newspaper articles, even if those documents were not already in "machine readable" form. This would permit computers to read, store, search, process and archieve a wide range of information without requiring information to be "keyed in" if it were already available on paper.
A reliable character recognition device could also be used to lower the cost of transmitting (and storing) documents that are currently processed by facsimile machines. Instead of transmitting values (of "lightness" or "darkness") for a document's "picture elements", a machine could "read" a document and transmit ascii codes for its characters.
The problem has many aspects, one of which is the fact that characters can be written in many fonts or styles, each varying significantly from the next. Thus, one person may construct an "A" with both its feet on the ground while another person will write the "A" leaning on one side or the other. Some people will make fat "A"s while others will make skinny "A"s. Thus, a general purpose character recognition arrangement must be capable of handling all such variations.
Existing pattern recognition schemes can be grouped into two general categories: the decision theoretic (or discriminant) approach and the syntactic (or structural) approach.
In the decision theoretic approach, a set of characteristic measurements, or features, is "extracted" from a pattern. Each pattern is represented by a feature vector and the recognition of characters is accomplished by partitioning the feature space.
The syntactic approach involves the recognition of a pattern's primitive "subpatterns". Subpatterns are recognized by the decision theoretic approach or by exact matching, and a description of the various subpatterns and their relationships is constructed. The description is "reduced" using the productions of a grammar in much the same way as a program written in a high-level language is reduced by a complier. The "single node" high level description that results from this reduction identifies the pattern.
The two approaches are similar in one respect. Both make use of a number of different microscopic views of a character rather than a single macroscopic view. As a result, either of these methods can "fail to see the forest for the trees". The decision theoretic approach has been criticized in the past for its failure to consider "structural information". The syntactic approach on the other hand attempts to make use of structural information but is based on the assumption that it is easier to recognize the subpatterns of patterns than it is to recognize the patterns themselves. Anyone who has attempted to decipher human handwriting can see the fallacy in this reasoning. In many cases, a letter can only be identified because of its position in a word. In the same way it seems reasonable to believe that a subpattern, like a corner or a bend, can only be correctly identified by considering its position in a surrounding character.